Overview

Dataset statistics

Number of variables32
Number of observations119390
Missing cells0
Missing cells (%)0.0%
Duplicate rows8178
Duplicate rows (%)6.8%
Total size in memory30.1 MiB
Average record size in memory264.0 B

Variable types

Categorical20
Numeric12

Warnings

Dataset has 8178 (6.8%) duplicate rowsDuplicates
country has a high cardinality: 178 distinct values High cardinality
reservation_status_date has a high cardinality: 926 distinct values High cardinality
reservation_status is highly correlated with is_canceledHigh correlation
is_canceled is highly correlated with reservation_statusHigh correlation
previous_cancellations is highly skewed (γ1 = 24.45804872) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.53979995) Skewed
lead_time has 6345 (5.3%) zeros Zeros
stays_in_weekend_nights has 51998 (43.6%) zeros Zeros
stays_in_week_nights has 7645 (6.4%) zeros Zeros
previous_cancellations has 112906 (94.6%) zeros Zeros
previous_bookings_not_canceled has 115770 (97.0%) zeros Zeros
booking_changes has 101314 (84.9%) zeros Zeros
days_in_waiting_list has 115692 (96.9%) zeros Zeros
adr has 1959 (1.6%) zeros Zeros
total_of_special_requests has 70318 (58.9%) zeros Zeros

Reproduction

Analysis started2021-05-09 07:53:42.150858
Analysis finished2021-05-09 07:54:37.345423
Duration55.19 seconds
Software versionpandas-profiling v2.13.0
Download configurationconfig.yaml

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Lisbon City Hotel
79330 
Algarve Resort Hotel
40060 

Length

Max length20
Median length17
Mean length18.00661697
Min length17

Characters and Unicode

Total characters2149810
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlgarve Resort Hotel
2nd rowAlgarve Resort Hotel
3rd rowAlgarve Resort Hotel
4th rowAlgarve Resort Hotel
5th rowAlgarve Resort Hotel
ValueCountFrequency (%)
Lisbon City Hotel79330
66.4%
Algarve Resort Hotel40060
33.6%
2021-05-09T00:54:37.599360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:37.698240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hotel119390
33.3%
city79330
22.1%
lisbon79330
22.1%
resort40060
 
11.2%
algarve40060
 
11.2%

Most occurring characters

ValueCountFrequency (%)
238780
11.1%
o238780
11.1%
t238780
11.1%
e199510
 
9.3%
l159450
 
7.4%
i158660
 
7.4%
s119390
 
5.6%
H119390
 
5.6%
r80120
 
3.7%
L79330
 
3.7%
Other values (9)517620
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1552860
72.2%
Uppercase Letter358170
 
16.7%
Space Separator238780
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
o238780
15.4%
t238780
15.4%
e199510
12.8%
l159450
10.3%
i158660
10.2%
s119390
7.7%
r80120
 
5.2%
b79330
 
5.1%
n79330
 
5.1%
y79330
 
5.1%
Other values (3)120180
7.7%
ValueCountFrequency (%)
H119390
33.3%
L79330
22.1%
C79330
22.1%
A40060
 
11.2%
R40060
 
11.2%
ValueCountFrequency (%)
238780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1911030
88.9%
Common238780
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
o238780
12.5%
t238780
12.5%
e199510
10.4%
l159450
 
8.3%
i158660
 
8.3%
s119390
 
6.2%
H119390
 
6.2%
r80120
 
4.2%
L79330
 
4.2%
b79330
 
4.2%
Other values (8)438290
22.9%
ValueCountFrequency (%)
238780
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2149810
100.0%

Most frequent character per block

ValueCountFrequency (%)
238780
11.1%
o238780
11.1%
t238780
11.1%
e199510
 
9.3%
l159450
 
7.4%
i158660
 
7.4%
s119390
 
5.6%
H119390
 
5.6%
r80120
 
3.7%
L79330
 
3.7%
Other values (9)517620
24.1%

is_canceled
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
75166 
1
44224 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
075166
63.0%
144224
37.0%
2021-05-09T00:54:37.912364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:37.993206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
075166
63.0%
144224
37.0%

Most occurring characters

ValueCountFrequency (%)
075166
63.0%
144224
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119390
100.0%

Most frequent character per category

ValueCountFrequency (%)
075166
63.0%
144224
37.0%

Most occurring scripts

ValueCountFrequency (%)
Common119390
100.0%

Most frequent character per script

ValueCountFrequency (%)
075166
63.0%
144224
37.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII119390
100.0%

Most frequent character per block

ValueCountFrequency (%)
075166
63.0%
144224
37.0%

lead_time
Real number (ℝ≥0)

ZEROS

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.0114164
Minimum0
Maximum737
Zeros6345
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:38.071857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3160
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)142

Descriptive statistics

Standard deviation106.863097
Coefficient of variation (CV)1.027416997
Kurtosis1.696448849
Mean104.0114164
Median Absolute Deviation (MAD)60
Skewness1.346549873
Sum12417923
Variance11419.72151
MonotonicityNot monotonic
2021-05-09T00:54:38.175289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
06345
 
5.3%
13460
 
2.9%
22069
 
1.7%
31816
 
1.5%
41715
 
1.4%
51565
 
1.3%
61445
 
1.2%
71331
 
1.1%
81138
 
1.0%
121079
 
0.9%
Other values (469)97427
81.6%
ValueCountFrequency (%)
06345
5.3%
13460
2.9%
22069
 
1.7%
31816
 
1.5%
41715
 
1.4%
ValueCountFrequency (%)
7371
 
< 0.1%
7091
 
< 0.1%
62917
< 0.1%
62630
< 0.1%
62217
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2016
56707 
2017
40687 
2015
21996 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters477560
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015
ValueCountFrequency (%)
201656707
47.5%
201740687
34.1%
201521996
 
18.4%
2021-05-09T00:54:38.435595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:38.508275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
201656707
47.5%
201740687
34.1%
201521996
 
18.4%

Most occurring characters

ValueCountFrequency (%)
2119390
25.0%
0119390
25.0%
1119390
25.0%
656707
11.9%
740687
 
8.5%
521996
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number477560
100.0%

Most frequent character per category

ValueCountFrequency (%)
2119390
25.0%
0119390
25.0%
1119390
25.0%
656707
11.9%
740687
 
8.5%
521996
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common477560
100.0%

Most frequent character per script

ValueCountFrequency (%)
2119390
25.0%
0119390
25.0%
1119390
25.0%
656707
11.9%
740687
 
8.5%
521996
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII477560
100.0%

Most frequent character per block

ValueCountFrequency (%)
2119390
25.0%
0119390
25.0%
1119390
25.0%
656707
11.9%
740687
 
8.5%
521996
 
4.6%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
August
13877 
July
12661 
May
11791 
October
11160 
April
11089 
Other values (7)
58812 

Length

Max length9
Median length6
Mean length5.903182846
Min length3

Characters and Unicode

Total characters704781
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly
ValueCountFrequency (%)
August13877
11.6%
July12661
10.6%
May11791
9.9%
October11160
9.3%
April11089
9.3%
June10939
9.2%
September10508
8.8%
March9794
8.2%
February8068
6.8%
November6794
5.7%
Other values (2)12709
10.6%
2021-05-09T00:54:38.781284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august13877
11.6%
july12661
10.6%
may11791
9.9%
october11160
9.3%
april11089
9.3%
june10939
9.2%
september10508
8.8%
march9794
8.2%
february8068
6.8%
november6794
5.7%
Other values (2)12709
10.6%

Most occurring characters

ValueCountFrequency (%)
e95619
13.6%
r78190
 
11.1%
u65351
 
9.3%
b43310
 
6.1%
a41511
 
5.9%
y38449
 
5.5%
t35545
 
5.0%
J29529
 
4.2%
c27734
 
3.9%
A24966
 
3.5%
Other values (16)224577
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter585391
83.1%
Uppercase Letter119390
 
16.9%

Most frequent character per category

ValueCountFrequency (%)
e95619
16.3%
r78190
13.4%
u65351
11.2%
b43310
 
7.4%
a41511
 
7.1%
y38449
 
6.6%
t35545
 
6.1%
c27734
 
4.7%
m24082
 
4.1%
l23750
 
4.1%
Other values (8)111850
19.1%
ValueCountFrequency (%)
J29529
24.7%
A24966
20.9%
M21585
18.1%
O11160
 
9.3%
S10508
 
8.8%
F8068
 
6.8%
N6794
 
5.7%
D6780
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin704781
100.0%

Most frequent character per script

ValueCountFrequency (%)
e95619
13.6%
r78190
 
11.1%
u65351
 
9.3%
b43310
 
6.1%
a41511
 
5.9%
y38449
 
5.5%
t35545
 
5.0%
J29529
 
4.2%
c27734
 
3.9%
A24966
 
3.5%
Other values (16)224577
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII704781
100.0%

Most frequent character per block

ValueCountFrequency (%)
e95619
13.6%
r78190
 
11.1%
u65351
 
9.3%
b43310
 
6.1%
a41511
 
5.9%
y38449
 
5.5%
t35545
 
5.0%
J29529
 
4.2%
c27734
 
3.9%
A24966
 
3.5%
Other values (16)224577
31.9%

arrival_date_week_number
Real number (ℝ≥0)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.16517296
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:38.892228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.60513836
Coefficient of variation (CV)0.500830176
Kurtosis-0.9860771763
Mean27.16517296
Median Absolute Deviation (MAD)11
Skewness-0.01001432604
Sum3243250
Variance185.0997897
MonotonicityNot monotonic
2021-05-09T00:54:38.993251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
333580
 
3.0%
303087
 
2.6%
323045
 
2.6%
343040
 
2.5%
182926
 
2.5%
212854
 
2.4%
282853
 
2.4%
172805
 
2.3%
202785
 
2.3%
292763
 
2.3%
Other values (43)89652
75.1%
ValueCountFrequency (%)
11047
0.9%
21218
1.0%
31319
1.1%
41487
1.2%
51387
1.2%
ValueCountFrequency (%)
531816
1.5%
521195
1.0%
51933
0.8%
501505
1.3%
491782
1.5%

arrival_date_day_of_month
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.79824106
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:39.112380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.780829471
Coefficient of variation (CV)0.5558105765
Kurtosis-1.187168319
Mean15.79824106
Median Absolute Deviation (MAD)8
Skewness-0.002000453979
Sum1886152
Variance77.10296619
MonotonicityNot monotonic
2021-05-09T00:54:39.215534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
174406
 
3.7%
54317
 
3.6%
154196
 
3.5%
254160
 
3.5%
264147
 
3.5%
94096
 
3.4%
124087
 
3.4%
164078
 
3.4%
24055
 
3.4%
194052
 
3.4%
Other values (21)77796
65.2%
ValueCountFrequency (%)
13626
3.0%
24055
3.4%
33855
3.2%
43763
3.2%
54317
3.6%
ValueCountFrequency (%)
312208
1.8%
303853
3.2%
293580
3.0%
283946
3.3%
273802
3.2%

stays_in_weekend_nights
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9275986264
Minimum0
Maximum19
Zeros51998
Zeros (%)43.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:39.326459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9986134946
Coefficient of variation (CV)1.076557755
Kurtosis7.174066064
Mean0.9275986264
Median Absolute Deviation (MAD)1
Skewness1.38004645
Sum110746
Variance0.9972289116
MonotonicityNot monotonic
2021-05-09T00:54:39.427773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
051998
43.6%
233308
27.9%
130626
25.7%
41855
 
1.6%
31259
 
1.1%
6153
 
0.1%
579
 
0.1%
860
 
0.1%
719
 
< 0.1%
911
 
< 0.1%
Other values (7)22
 
< 0.1%
ValueCountFrequency (%)
051998
43.6%
130626
25.7%
233308
27.9%
31259
 
1.1%
41855
 
1.6%
ValueCountFrequency (%)
191
 
< 0.1%
181
 
< 0.1%
163
< 0.1%
142
< 0.1%
133
< 0.1%

stays_in_week_nights
Real number (ℝ≥0)

ZEROS

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.500301533
Minimum0
Maximum50
Zeros7645
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:39.548758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.908285615
Coefficient of variation (CV)0.7632221914
Kurtosis24.28455482
Mean2.500301533
Median Absolute Deviation (MAD)1
Skewness2.862249242
Sum298511
Variance3.641553989
MonotonicityNot monotonic
2021-05-09T00:54:39.649593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
233684
28.2%
130310
25.4%
322258
18.6%
511077
 
9.3%
49563
 
8.0%
07645
 
6.4%
61499
 
1.3%
101036
 
0.9%
71029
 
0.9%
8656
 
0.5%
Other values (25)633
 
0.5%
ValueCountFrequency (%)
07645
 
6.4%
130310
25.4%
233684
28.2%
322258
18.6%
49563
 
8.0%
ValueCountFrequency (%)
501
< 0.1%
421
< 0.1%
411
< 0.1%
402
< 0.1%
351
< 0.1%

adults
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.856403384
Minimum0
Maximum55
Zeros403
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:39.760617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5792609988
Coefficient of variation (CV)0.3120340137
Kurtosis1352.115116
Mean1.856403384
Median Absolute Deviation (MAD)0
Skewness18.31780476
Sum221636
Variance0.3355433048
MonotonicityNot monotonic
2021-05-09T00:54:39.851543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
289680
75.1%
123027
 
19.3%
36202
 
5.2%
0403
 
0.3%
462
 
0.1%
265
 
< 0.1%
272
 
< 0.1%
202
 
< 0.1%
52
 
< 0.1%
551
 
< 0.1%
Other values (4)4
 
< 0.1%
ValueCountFrequency (%)
0403
 
0.3%
123027
 
19.3%
289680
75.1%
36202
 
5.2%
462
 
0.1%
ValueCountFrequency (%)
551
 
< 0.1%
501
 
< 0.1%
401
 
< 0.1%
272
 
< 0.1%
265
< 0.1%

children
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0.0
110800 
1.0
 
4861
2.0
 
3652
3.0
 
76
10.0
 
1

Length

Max length4
Median length3
Mean length3.000008376
Min length3

Characters and Unicode

Total characters358171
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0110800
92.8%
1.04861
 
4.1%
2.03652
 
3.1%
3.076
 
0.1%
10.01
 
< 0.1%
2021-05-09T00:54:40.115488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:40.206260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0110800
92.8%
1.04861
 
4.1%
2.03652
 
3.1%
3.076
 
0.1%
10.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0230191
64.3%
.119390
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number238781
66.7%
Other Punctuation119390
33.3%

Most frequent character per category

ValueCountFrequency (%)
0230191
96.4%
14862
 
2.0%
23652
 
1.5%
376
 
< 0.1%
ValueCountFrequency (%)
.119390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common358171
100.0%

Most frequent character per script

ValueCountFrequency (%)
0230191
64.3%
.119390
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII358171
100.0%

Most frequent character per block

ValueCountFrequency (%)
0230191
64.3%
.119390
33.3%
14862
 
1.4%
23652
 
1.0%
376
 
< 0.1%

babies
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
118473 
1
 
900
2
 
15
9
 
1
10
 
1

Length

Max length2
Median length1
Mean length1.000008376
Min length1

Characters and Unicode

Total characters119391
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0118473
99.2%
1900
 
0.8%
215
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
2021-05-09T00:54:40.488921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:40.580120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0118473
99.2%
1900
 
0.8%
215
 
< 0.1%
101
 
< 0.1%
91
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0118474
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119391
100.0%

Most frequent character per category

ValueCountFrequency (%)
0118474
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common119391
100.0%

Most frequent character per script

ValueCountFrequency (%)
0118474
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII119391
100.0%

Most frequent character per block

ValueCountFrequency (%)
0118474
99.2%
1901
 
0.8%
215
 
< 0.1%
91
 
< 0.1%

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
BB
92310 
HB
14463 
SC
10650 
Undefined
 
1169
FB
 
798

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1074510
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB
ValueCountFrequency (%)
BB 92310
77.3%
HB 14463
 
12.1%
SC 10650
 
8.9%
Undefined1169
 
1.0%
FB 798
 
0.7%
2021-05-09T00:54:40.832271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:40.921274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
bb92310
77.3%
hb14463
 
12.1%
sc10650
 
8.9%
undefined1169
 
1.0%
fb798
 
0.7%

Most occurring characters

ValueCountFrequency (%)
827547
77.0%
B199881
 
18.6%
H14463
 
1.3%
S10650
 
1.0%
C10650
 
1.0%
n2338
 
0.2%
d2338
 
0.2%
e2338
 
0.2%
U1169
 
0.1%
f1169
 
0.1%
Other values (2)1967
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator827547
77.0%
Uppercase Letter237611
 
22.1%
Lowercase Letter9352
 
0.9%

Most frequent character per category

ValueCountFrequency (%)
B199881
84.1%
H14463
 
6.1%
S10650
 
4.5%
C10650
 
4.5%
U1169
 
0.5%
F798
 
0.3%
ValueCountFrequency (%)
n2338
25.0%
d2338
25.0%
e2338
25.0%
f1169
12.5%
i1169
12.5%
ValueCountFrequency (%)
827547
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common827547
77.0%
Latin246963
 
23.0%

Most frequent character per script

ValueCountFrequency (%)
B199881
80.9%
H14463
 
5.9%
S10650
 
4.3%
C10650
 
4.3%
n2338
 
0.9%
d2338
 
0.9%
e2338
 
0.9%
U1169
 
0.5%
f1169
 
0.5%
i1169
 
0.5%
ValueCountFrequency (%)
827547
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074510
100.0%

Most frequent character per block

ValueCountFrequency (%)
827547
77.0%
B199881
 
18.6%
H14463
 
1.3%
S10650
 
1.0%
C10650
 
1.0%
n2338
 
0.2%
d2338
 
0.2%
e2338
 
0.2%
U1169
 
0.1%
f1169
 
0.1%
Other values (2)1967
 
0.2%

country
Categorical

HIGH CARDINALITY

Distinct178
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
PRT
48590 
GBR
12129 
FRA
10415 
ESP
8568 
DEU
7287 
Other values (173)
32401 

Length

Max length3
Median length3
Mean length2.98928721
Min length2

Characters and Unicode

Total characters356891
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
PRT48590
40.7%
GBR12129
 
10.2%
FRA10415
 
8.7%
ESP8568
 
7.2%
DEU7287
 
6.1%
ITA3766
 
3.2%
IRL3375
 
2.8%
BEL2342
 
2.0%
BRA2224
 
1.9%
NLD2104
 
1.8%
Other values (168)18590
 
15.6%
2021-05-09T00:54:41.176148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
prt48590
40.7%
gbr12129
 
10.2%
fra10415
 
8.7%
esp8568
 
7.2%
deu7287
 
6.1%
ita3766
 
3.2%
irl3375
 
2.8%
bel2342
 
2.0%
bra2224
 
1.9%
nld2104
 
1.8%
Other values (168)18590
 
15.6%

Most occurring characters

ValueCountFrequency (%)
R80804
22.6%
P58506
16.4%
T54263
15.2%
A21627
 
6.1%
E21538
 
6.0%
B17051
 
4.8%
S13931
 
3.9%
U13781
 
3.9%
G13130
 
3.7%
F10956
 
3.1%
Other values (16)51304
14.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter356891
100.0%

Most frequent character per category

ValueCountFrequency (%)
R80804
22.6%
P58506
16.4%
T54263
15.2%
A21627
 
6.1%
E21538
 
6.0%
B17051
 
4.8%
S13931
 
3.9%
U13781
 
3.9%
G13130
 
3.7%
F10956
 
3.1%
Other values (16)51304
14.4%

Most occurring scripts

ValueCountFrequency (%)
Latin356891
100.0%

Most frequent character per script

ValueCountFrequency (%)
R80804
22.6%
P58506
16.4%
T54263
15.2%
A21627
 
6.1%
E21538
 
6.0%
B17051
 
4.8%
S13931
 
3.9%
U13781
 
3.9%
G13130
 
3.7%
F10956
 
3.1%
Other values (16)51304
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII356891
100.0%

Most frequent character per block

ValueCountFrequency (%)
R80804
22.6%
P58506
16.4%
T54263
15.2%
A21627
 
6.1%
E21538
 
6.0%
B17051
 
4.8%
S13931
 
3.9%
U13781
 
3.9%
G13130
 
3.7%
F10956
 
3.1%
Other values (16)51304
14.4%

market_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Online TA
56477 
Offline TA/TO
24219 
Groups
19811 
Direct
12606 
Corporate
 
5295
Other values (3)
 
982

Length

Max length13
Median length9
Mean length9.01976715
Min length6

Characters and Unicode

Total characters1076870
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA
ValueCountFrequency (%)
Online TA56477
47.3%
Offline TA/TO24219
20.3%
Groups19811
 
16.6%
Direct12606
 
10.6%
Corporate5295
 
4.4%
Complementary743
 
0.6%
Aviation237
 
0.2%
Undefined2
 
< 0.1%
2021-05-09T00:54:41.458744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:41.559560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
online56477
28.2%
ta56477
28.2%
ta/to24219
12.1%
offline24219
12.1%
groups19811
 
9.9%
direct12606
 
6.3%
corporate5295
 
2.6%
complementary743
 
0.4%
aviation237
 
0.1%
undefined2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n138157
12.8%
O104915
9.7%
T104915
9.7%
e100087
9.3%
i93778
8.7%
l81439
7.6%
A80933
7.5%
80696
7.5%
f48440
 
4.5%
r43750
 
4.1%
Other values (16)199760
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter642735
59.7%
Uppercase Letter329220
30.6%
Space Separator80696
 
7.5%
Other Punctuation24219
 
2.2%

Most frequent character per category

ValueCountFrequency (%)
n138157
21.5%
e100087
15.6%
i93778
14.6%
l81439
12.7%
f48440
 
7.5%
r43750
 
6.8%
o31381
 
4.9%
p25849
 
4.0%
u19811
 
3.1%
s19811
 
3.1%
Other values (7)40232
 
6.3%
ValueCountFrequency (%)
O104915
31.9%
T104915
31.9%
A80933
24.6%
G19811
 
6.0%
D12606
 
3.8%
C6038
 
1.8%
U2
 
< 0.1%
ValueCountFrequency (%)
80696
100.0%
ValueCountFrequency (%)
/24219
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin971955
90.3%
Common104915
 
9.7%

Most frequent character per script

ValueCountFrequency (%)
n138157
14.2%
O104915
10.8%
T104915
10.8%
e100087
10.3%
i93778
9.6%
l81439
8.4%
A80933
8.3%
f48440
 
5.0%
r43750
 
4.5%
o31381
 
3.2%
Other values (14)144160
14.8%
ValueCountFrequency (%)
80696
76.9%
/24219
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1076870
100.0%

Most frequent character per block

ValueCountFrequency (%)
n138157
12.8%
O104915
9.7%
T104915
9.7%
e100087
9.3%
i93778
8.7%
l81439
7.6%
A80933
7.5%
80696
7.5%
f48440
 
4.5%
r43750
 
4.1%
Other values (16)199760
18.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
TA/TO
97870 
Direct
14645 
Corporate
 
6677
GDS
 
193
Undefined
 
5

Length

Max length9
Median length5
Mean length5.343303459
Min length3

Characters and Unicode

Total characters637937
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO
ValueCountFrequency (%)
TA/TO97870
82.0%
Direct14645
 
12.3%
Corporate6677
 
5.6%
GDS193
 
0.2%
Undefined5
 
< 0.1%
2021-05-09T00:54:41.882692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:41.963427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
ta/to97870
82.0%
direct14645
 
12.3%
corporate6677
 
5.6%
gds193
 
0.2%
undefined5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T195740
30.7%
A97870
15.3%
/97870
15.3%
O97870
15.3%
r27999
 
4.4%
e21332
 
3.3%
t21322
 
3.3%
D14838
 
2.3%
i14650
 
2.3%
c14645
 
2.3%
Other values (10)33801
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter413386
64.8%
Lowercase Letter126681
 
19.9%
Other Punctuation97870
 
15.3%

Most frequent character per category

ValueCountFrequency (%)
r27999
22.1%
e21332
16.8%
t21322
16.8%
i14650
11.6%
c14645
11.6%
o13354
10.5%
p6677
 
5.3%
a6677
 
5.3%
n10
 
< 0.1%
d10
 
< 0.1%
ValueCountFrequency (%)
T195740
47.4%
A97870
23.7%
O97870
23.7%
D14838
 
3.6%
C6677
 
1.6%
G193
 
< 0.1%
S193
 
< 0.1%
U5
 
< 0.1%
ValueCountFrequency (%)
/97870
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin540067
84.7%
Common97870
 
15.3%

Most frequent character per script

ValueCountFrequency (%)
T195740
36.2%
A97870
18.1%
O97870
18.1%
r27999
 
5.2%
e21332
 
3.9%
t21322
 
3.9%
D14838
 
2.7%
i14650
 
2.7%
c14645
 
2.7%
o13354
 
2.5%
Other values (9)20447
 
3.8%
ValueCountFrequency (%)
/97870
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII637937
100.0%

Most frequent character per block

ValueCountFrequency (%)
T195740
30.7%
A97870
15.3%
/97870
15.3%
O97870
15.3%
r27999
 
4.4%
e21332
 
3.3%
t21322
 
3.3%
D14838
 
2.3%
i14650
 
2.3%
c14645
 
2.3%
Other values (10)33801
 
5.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
115580 
1
 
3810

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0115580
96.8%
13810
 
3.2%
2021-05-09T00:54:42.225981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:42.306714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0115580
96.8%
13810
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0115580
96.8%
13810
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119390
100.0%

Most frequent character per category

ValueCountFrequency (%)
0115580
96.8%
13810
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common119390
100.0%

Most frequent character per script

ValueCountFrequency (%)
0115580
96.8%
13810
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII119390
100.0%

Most frequent character per block

ValueCountFrequency (%)
0115580
96.8%
13810
 
3.2%

previous_cancellations
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08711784907
Minimum0
Maximum26
Zeros112906
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:42.387983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8443363842
Coefficient of variation (CV)9.691887405
Kurtosis674.0736926
Mean0.08711784907
Median Absolute Deviation (MAD)0
Skewness24.45804872
Sum10401
Variance0.7129039296
MonotonicityNot monotonic
2021-05-09T00:54:42.488906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0112906
94.6%
16051
 
5.1%
2116
 
0.1%
365
 
0.1%
2448
 
< 0.1%
1135
 
< 0.1%
431
 
< 0.1%
2626
 
< 0.1%
2525
 
< 0.1%
622
 
< 0.1%
Other values (5)65
 
0.1%
ValueCountFrequency (%)
0112906
94.6%
16051
 
5.1%
2116
 
0.1%
365
 
0.1%
431
 
< 0.1%
ValueCountFrequency (%)
2626
< 0.1%
2525
< 0.1%
2448
< 0.1%
211
 
< 0.1%
1919
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1370969093
Minimum0
Maximum72
Zeros115770
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:42.599987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.497436848
Coefficient of variation (CV)10.92246977
Kurtosis767.2452097
Mean0.1370969093
Median Absolute Deviation (MAD)0
Skewness23.53979995
Sum16368
Variance2.242317113
MonotonicityNot monotonic
2021-05-09T00:54:42.698727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0115770
97.0%
11542
 
1.3%
2580
 
0.5%
3333
 
0.3%
4229
 
0.2%
5181
 
0.2%
6115
 
0.1%
788
 
0.1%
870
 
0.1%
960
 
0.1%
Other values (63)422
 
0.4%
ValueCountFrequency (%)
0115770
97.0%
11542
 
1.3%
2580
 
0.5%
3333
 
0.3%
4229
 
0.2%
ValueCountFrequency (%)
721
< 0.1%
711
< 0.1%
701
< 0.1%
691
< 0.1%
681
< 0.1%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
A
85994 
D
19201 
E
 
6535
F
 
2897
G
 
2094
Other values (5)
 
2669

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1910240
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
A 85994
72.0%
D 19201
 
16.1%
E 6535
 
5.5%
F 2897
 
2.4%
G 2094
 
1.8%
B 1118
 
0.9%
C 932
 
0.8%
H 601
 
0.5%
P 12
 
< 0.1%
L 6
 
< 0.1%
2021-05-09T00:54:43.180961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:43.277944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
a85994
72.0%
d19201
 
16.1%
e6535
 
5.5%
f2897
 
2.4%
g2094
 
1.8%
b1118
 
0.9%
c932
 
0.8%
h601
 
0.5%
p12
 
< 0.1%
l6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1790850
93.8%
A85994
 
4.5%
D19201
 
1.0%
E6535
 
0.3%
F2897
 
0.2%
G2094
 
0.1%
B1118
 
0.1%
C932
 
< 0.1%
H601
 
< 0.1%
P12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator1790850
93.8%
Uppercase Letter119390
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A85994
72.0%
D19201
 
16.1%
E6535
 
5.5%
F2897
 
2.4%
G2094
 
1.8%
B1118
 
0.9%
C932
 
0.8%
H601
 
0.5%
P12
 
< 0.1%
L6
 
< 0.1%
ValueCountFrequency (%)
1790850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1790850
93.8%
Latin119390
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
A85994
72.0%
D19201
 
16.1%
E6535
 
5.5%
F2897
 
2.4%
G2094
 
1.8%
B1118
 
0.9%
C932
 
0.8%
H601
 
0.5%
P12
 
< 0.1%
L6
 
< 0.1%
ValueCountFrequency (%)
1790850
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1910240
100.0%

Most frequent character per block

ValueCountFrequency (%)
1790850
93.8%
A85994
 
4.5%
D19201
 
1.0%
E6535
 
0.3%
F2897
 
0.2%
G2094
 
0.1%
B1118
 
0.1%
C932
 
< 0.1%
H601
 
< 0.1%
P12
 
< 0.1%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
A
74053 
D
25322 
E
7806 
F
 
3751
G
 
2553
Other values (7)
 
5905

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters1910240
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA
ValueCountFrequency (%)
A 74053
62.0%
D 25322
 
21.2%
E 7806
 
6.5%
F 3751
 
3.1%
G 2553
 
2.1%
C 2375
 
2.0%
B 2163
 
1.8%
H 712
 
0.6%
I 363
 
0.3%
K 279
 
0.2%
Other values (2)13
 
< 0.1%
2021-05-09T00:54:43.621590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a74053
62.0%
d25322
 
21.2%
e7806
 
6.5%
f3751
 
3.1%
g2553
 
2.1%
c2375
 
2.0%
b2163
 
1.8%
h712
 
0.6%
i363
 
0.3%
k279
 
0.2%
Other values (2)13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1790850
93.8%
A74053
 
3.9%
D25322
 
1.3%
E7806
 
0.4%
F3751
 
0.2%
G2553
 
0.1%
C2375
 
0.1%
B2163
 
0.1%
H712
 
< 0.1%
I363
 
< 0.1%
Other values (3)292
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator1790850
93.8%
Uppercase Letter119390
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
A74053
62.0%
D25322
 
21.2%
E7806
 
6.5%
F3751
 
3.1%
G2553
 
2.1%
C2375
 
2.0%
B2163
 
1.8%
H712
 
0.6%
I363
 
0.3%
K279
 
0.2%
Other values (2)13
 
< 0.1%
ValueCountFrequency (%)
1790850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1790850
93.8%
Latin119390
 
6.2%

Most frequent character per script

ValueCountFrequency (%)
A74053
62.0%
D25322
 
21.2%
E7806
 
6.5%
F3751
 
3.1%
G2553
 
2.1%
C2375
 
2.0%
B2163
 
1.8%
H712
 
0.6%
I363
 
0.3%
K279
 
0.2%
Other values (2)13
 
< 0.1%
ValueCountFrequency (%)
1790850
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1910240
100.0%

Most frequent character per block

ValueCountFrequency (%)
1790850
93.8%
A74053
 
3.9%
D25322
 
1.3%
E7806
 
0.4%
F3751
 
0.2%
G2553
 
0.1%
C2375
 
0.1%
B2163
 
0.1%
H712
 
< 0.1%
I363
 
< 0.1%
Other values (3)292
 
< 0.1%

booking_changes
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2211240472
Minimum0
Maximum21
Zeros101314
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:43.732549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6523055727
Coefficient of variation (CV)2.949953118
Kurtosis79.39360467
Mean0.2211240472
Median Absolute Deviation (MAD)0
Skewness6.000270054
Sum26400
Variance0.4255025601
MonotonicityNot monotonic
2021-05-09T00:54:43.831407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0101314
84.9%
112701
 
10.6%
23805
 
3.2%
3927
 
0.8%
4376
 
0.3%
5118
 
0.1%
663
 
0.1%
731
 
< 0.1%
817
 
< 0.1%
98
 
< 0.1%
Other values (11)30
 
< 0.1%
ValueCountFrequency (%)
0101314
84.9%
112701
 
10.6%
23805
 
3.2%
3927
 
0.8%
4376
 
0.3%
ValueCountFrequency (%)
211
< 0.1%
201
< 0.1%
181
< 0.1%
172
< 0.1%
162
< 0.1%

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
No Deposit
104641 
Non Refund
14587 
Refundable
 
162

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1790850
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit
ValueCountFrequency (%)
No Deposit 104641
87.6%
Non Refund 14587
 
12.2%
Refundable 162
 
0.1%
2021-05-09T00:54:44.084030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:44.166914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
deposit104641
43.9%
no104641
43.9%
refund14587
 
6.1%
non14587
 
6.1%
refundable162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
716178
40.0%
o223869
 
12.5%
e119552
 
6.7%
N119228
 
6.7%
D104641
 
5.8%
p104641
 
5.8%
s104641
 
5.8%
i104641
 
5.8%
t104641
 
5.8%
n29336
 
1.6%
Other values (7)59482
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter836054
46.7%
Space Separator716178
40.0%
Uppercase Letter238618
 
13.3%

Most frequent character per category

ValueCountFrequency (%)
o223869
26.8%
e119552
14.3%
p104641
12.5%
s104641
12.5%
i104641
12.5%
t104641
12.5%
n29336
 
3.5%
f14749
 
1.8%
u14749
 
1.8%
d14749
 
1.8%
Other values (3)486
 
0.1%
ValueCountFrequency (%)
N119228
50.0%
D104641
43.9%
R14749
 
6.2%
ValueCountFrequency (%)
716178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1074672
60.0%
Common716178
40.0%

Most frequent character per script

ValueCountFrequency (%)
o223869
20.8%
e119552
11.1%
N119228
11.1%
D104641
9.7%
p104641
9.7%
s104641
9.7%
i104641
9.7%
t104641
9.7%
n29336
 
2.7%
R14749
 
1.4%
Other values (6)44733
 
4.2%
ValueCountFrequency (%)
716178
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1790850
100.0%

Most frequent character per block

ValueCountFrequency (%)
716178
40.0%
o223869
 
12.5%
e119552
 
6.7%
N119228
 
6.7%
D104641
 
5.8%
p104641
 
5.8%
s104641
 
5.8%
i104641
 
5.8%
t104641
 
5.8%
n29336
 
1.6%
Other values (7)59482
 
3.3%

agent
Categorical

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Other
36637 
9
31961 
no_agent
16340 
240
13922 
1
7191 
Other values (4)
13339 

Length

Max length15
Median length11
Mean length9.706240054
Min length5

Characters and Unicode

Total characters1158828
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row no_agent
2nd row no_agent
3rd row no_agent
4th rowOther
5th row 240
ValueCountFrequency (%)
Other36637
30.7%
931961
26.8%
no_agent16340
13.7%
24013922
 
11.7%
17191
 
6.0%
143640
 
3.0%
73539
 
3.0%
63290
 
2.8%
2502870
 
2.4%
2021-05-09T00:54:44.429815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:44.520756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other36637
30.7%
931961
26.8%
no_agent16340
13.7%
24013922
 
11.7%
17191
 
6.0%
143640
 
3.0%
73539
 
3.0%
63290
 
2.8%
2502870
 
2.4%

Most occurring characters

ValueCountFrequency (%)
741286
64.0%
e52977
 
4.6%
t52977
 
4.6%
O36637
 
3.2%
h36637
 
3.2%
r36637
 
3.2%
n32680
 
2.8%
931961
 
2.8%
417562
 
1.5%
216792
 
1.4%
Other values (9)102682
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator741286
64.0%
Lowercase Letter260928
 
22.5%
Decimal Number103637
 
8.9%
Uppercase Letter36637
 
3.2%
Connector Punctuation16340
 
1.4%

Most frequent character per category

ValueCountFrequency (%)
e52977
20.3%
t52977
20.3%
h36637
14.0%
r36637
14.0%
n32680
12.5%
o16340
 
6.3%
a16340
 
6.3%
g16340
 
6.3%
ValueCountFrequency (%)
931961
30.8%
417562
16.9%
216792
16.2%
016792
16.2%
110831
 
10.5%
73539
 
3.4%
63290
 
3.2%
52870
 
2.8%
ValueCountFrequency (%)
741286
100.0%
ValueCountFrequency (%)
_16340
100.0%
ValueCountFrequency (%)
O36637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common861263
74.3%
Latin297565
 
25.7%

Most frequent character per script

ValueCountFrequency (%)
741286
86.1%
931961
 
3.7%
417562
 
2.0%
216792
 
1.9%
016792
 
1.9%
_16340
 
1.9%
110831
 
1.3%
73539
 
0.4%
63290
 
0.4%
52870
 
0.3%
ValueCountFrequency (%)
e52977
17.8%
t52977
17.8%
O36637
12.3%
h36637
12.3%
r36637
12.3%
n32680
11.0%
o16340
 
5.5%
a16340
 
5.5%
g16340
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1158828
100.0%

Most frequent character per block

ValueCountFrequency (%)
741286
64.0%
e52977
 
4.6%
t52977
 
4.6%
O36637
 
3.2%
h36637
 
3.2%
r36637
 
3.2%
n32680
 
2.8%
931961
 
2.8%
417562
 
1.5%
216792
 
1.4%
Other values (9)102682
 
8.9%

company
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
no_company
112593 
Other
 
5086
40
 
927
223
 
784

Length

Max length17
Median length17
Mean length16.40281431
Min length5

Characters and Unicode

Total characters1958332
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row no_company
2nd row no_company
3rd row no_company
4th row no_company
5th row no_company
ValueCountFrequency (%)
no_company112593
94.3%
Other5086
 
4.3%
40927
 
0.8%
223784
 
0.7%
2021-05-09T00:54:44.841760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:44.924464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
no_company112593
94.3%
other5086
 
4.3%
40927
 
0.8%
223784
 
0.7%

Most occurring characters

ValueCountFrequency (%)
802766
41.0%
n225186
 
11.5%
o225186
 
11.5%
_112593
 
5.7%
c112593
 
5.7%
m112593
 
5.7%
p112593
 
5.7%
a112593
 
5.7%
y112593
 
5.7%
O5086
 
0.3%
Other values (8)24550
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1033681
52.8%
Space Separator802766
41.0%
Connector Punctuation112593
 
5.7%
Uppercase Letter5086
 
0.3%
Decimal Number4206
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
n225186
21.8%
o225186
21.8%
c112593
10.9%
m112593
10.9%
p112593
10.9%
a112593
10.9%
y112593
10.9%
t5086
 
0.5%
h5086
 
0.5%
e5086
 
0.5%
ValueCountFrequency (%)
21568
37.3%
4927
22.0%
0927
22.0%
3784
18.6%
ValueCountFrequency (%)
802766
100.0%
ValueCountFrequency (%)
_112593
100.0%
ValueCountFrequency (%)
O5086
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1038767
53.0%
Common919565
47.0%

Most frequent character per script

ValueCountFrequency (%)
n225186
21.7%
o225186
21.7%
c112593
10.8%
m112593
10.8%
p112593
10.8%
a112593
10.8%
y112593
10.8%
O5086
 
0.5%
t5086
 
0.5%
h5086
 
0.5%
Other values (2)10172
 
1.0%
ValueCountFrequency (%)
802766
87.3%
_112593
 
12.2%
21568
 
0.2%
4927
 
0.1%
0927
 
0.1%
3784
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1958332
100.0%

Most frequent character per block

ValueCountFrequency (%)
802766
41.0%
n225186
 
11.5%
o225186
 
11.5%
_112593
 
5.7%
c112593
 
5.7%
m112593
 
5.7%
p112593
 
5.7%
a112593
 
5.7%
y112593
 
5.7%
O5086
 
0.3%
Other values (8)24550
 
1.3%

days_in_waiting_list
Real number (ℝ≥0)

ZEROS

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.321149175
Minimum0
Maximum391
Zeros115692
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:45.033287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.59472088
Coefficient of variation (CV)7.580176694
Kurtosis186.7930696
Mean2.321149175
Median Absolute Deviation (MAD)0
Skewness11.94435345
Sum277122
Variance309.5742028
MonotonicityNot monotonic
2021-05-09T00:54:45.136237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0115692
96.9%
39227
 
0.2%
58164
 
0.1%
44141
 
0.1%
31127
 
0.1%
3596
 
0.1%
4694
 
0.1%
6989
 
0.1%
6383
 
0.1%
5080
 
0.1%
Other values (118)2597
 
2.2%
ValueCountFrequency (%)
0115692
96.9%
112
 
< 0.1%
25
 
< 0.1%
359
 
< 0.1%
425
 
< 0.1%
ValueCountFrequency (%)
39145
< 0.1%
37915
 
< 0.1%
33015
 
< 0.1%
25910
 
< 0.1%
23635
< 0.1%

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Transient
89613 
Transient-Party
25124 
Contract
 
4076
Group
 
577

Length

Max length15
Median length9
Mean length10.20914649
Min length5

Characters and Unicode

Total characters1218870
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient
ValueCountFrequency (%)
Transient89613
75.1%
Transient-Party25124
 
21.0%
Contract4076
 
3.4%
Group577
 
0.5%
2021-05-09T00:54:45.398823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:45.480001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
transient89613
75.1%
transient-party25124
 
21.0%
contract4076
 
3.4%
group577
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n233550
19.2%
t148013
12.1%
r144514
11.9%
a143937
11.8%
T114737
9.4%
s114737
9.4%
i114737
9.4%
e114737
9.4%
-25124
 
2.1%
P25124
 
2.1%
Other values (7)39660
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1049232
86.1%
Uppercase Letter144514
 
11.9%
Dash Punctuation25124
 
2.1%

Most frequent character per category

ValueCountFrequency (%)
n233550
22.3%
t148013
14.1%
r144514
13.8%
a143937
13.7%
s114737
10.9%
i114737
10.9%
e114737
10.9%
y25124
 
2.4%
o4653
 
0.4%
c4076
 
0.4%
Other values (2)1154
 
0.1%
ValueCountFrequency (%)
T114737
79.4%
P25124
 
17.4%
C4076
 
2.8%
G577
 
0.4%
ValueCountFrequency (%)
-25124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1193746
97.9%
Common25124
 
2.1%

Most frequent character per script

ValueCountFrequency (%)
n233550
19.6%
t148013
12.4%
r144514
12.1%
a143937
12.1%
T114737
9.6%
s114737
9.6%
i114737
9.6%
e114737
9.6%
P25124
 
2.1%
y25124
 
2.1%
Other values (6)14536
 
1.2%
ValueCountFrequency (%)
-25124
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1218870
100.0%

Most frequent character per block

ValueCountFrequency (%)
n233550
19.2%
t148013
12.1%
r144514
11.9%
a143937
11.8%
T114737
9.4%
s114737
9.4%
i114737
9.4%
e114737
9.4%
-25124
 
2.1%
P25124
 
2.1%
Other values (7)39660
 
3.3%

adr
Real number (ℝ)

ZEROS

Distinct8879
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.8311215
Minimum-6.38
Maximum5400
Zeros1959
Zeros (%)1.6%
Negative1
Negative (%)< 0.1%
Memory size1.8 MiB
2021-05-09T00:54:45.590921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile38.4
Q169.29
median94.575
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.71

Descriptive statistics

Standard deviation50.53579029
Coefficient of variation (CV)0.4962705853
Kurtosis1013.189851
Mean101.8311215
Median Absolute Deviation (MAD)27.825
Skewness10.53021398
Sum12157617.6
Variance2553.8661
MonotonicityNot monotonic
2021-05-09T00:54:45.691793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
623754
 
3.1%
752715
 
2.3%
902473
 
2.1%
652418
 
2.0%
01959
 
1.6%
801889
 
1.6%
951661
 
1.4%
1201607
 
1.3%
1001573
 
1.3%
851538
 
1.3%
Other values (8869)97803
81.9%
ValueCountFrequency (%)
-6.381
 
< 0.1%
01959
1.6%
0.261
 
< 0.1%
0.51
 
< 0.1%
115
 
< 0.1%
ValueCountFrequency (%)
54001
< 0.1%
5101
< 0.1%
5081
< 0.1%
451.51
< 0.1%
4501
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
111974 
1
 
7383
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119390
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0111974
93.8%
17383
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%
2021-05-09T00:54:45.954116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:46.044464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0111974
93.8%
17383
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0111974
93.8%
17383
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number119390
100.0%

Most frequent character per category

ValueCountFrequency (%)
0111974
93.8%
17383
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common119390
100.0%

Most frequent character per script

ValueCountFrequency (%)
0111974
93.8%
17383
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII119390
100.0%

Most frequent character per block

ValueCountFrequency (%)
0111974
93.8%
17383
 
6.2%
228
 
< 0.1%
33
 
< 0.1%
82
 
< 0.1%

total_of_special_requests
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5713627607
Minimum0
Maximum5
Zeros70318
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-05-09T00:54:46.167611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7927984228
Coefficient of variation (CV)1.387557043
Kurtosis1.492564811
Mean0.5713627607
Median Absolute Deviation (MAD)0
Skewness1.349189377
Sum68215
Variance0.6285293392
MonotonicityNot monotonic
2021-05-09T00:54:46.268536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
070318
58.9%
133226
27.8%
212969
 
10.9%
32497
 
2.1%
4340
 
0.3%
540
 
< 0.1%
ValueCountFrequency (%)
070318
58.9%
133226
27.8%
212969
 
10.9%
32497
 
2.1%
4340
 
0.3%
ValueCountFrequency (%)
540
 
< 0.1%
4340
 
0.3%
32497
 
2.1%
212969
 
10.9%
133226
27.8%

reservation_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Check-Out
75166 
Canceled
43017 
No-Show
 
1207

Length

Max length9
Median length9
Mean length8.619473993
Min length7

Characters and Unicode

Total characters1029079
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out
ValueCountFrequency (%)
Check-Out75166
63.0%
Canceled43017
36.0%
No-Show1207
 
1.0%
2021-05-09T00:54:46.541298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-05-09T00:54:46.642724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
check-out75166
63.0%
canceled43017
36.0%
no-show1207
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e161200
15.7%
C118183
11.5%
c118183
11.5%
h76373
7.4%
-76373
7.4%
k75166
7.3%
O75166
7.3%
u75166
7.3%
t75166
7.3%
a43017
 
4.2%
Other values (7)135086
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter756943
73.6%
Uppercase Letter195763
 
19.0%
Dash Punctuation76373
 
7.4%

Most frequent character per category

ValueCountFrequency (%)
e161200
21.3%
c118183
15.6%
h76373
10.1%
k75166
9.9%
u75166
9.9%
t75166
9.9%
a43017
 
5.7%
n43017
 
5.7%
l43017
 
5.7%
d43017
 
5.7%
Other values (2)3621
 
0.5%
ValueCountFrequency (%)
C118183
60.4%
O75166
38.4%
N1207
 
0.6%
S1207
 
0.6%
ValueCountFrequency (%)
-76373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin952706
92.6%
Common76373
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
e161200
16.9%
C118183
12.4%
c118183
12.4%
h76373
8.0%
k75166
7.9%
O75166
7.9%
u75166
7.9%
t75166
7.9%
a43017
 
4.5%
n43017
 
4.5%
Other values (6)92069
9.7%
ValueCountFrequency (%)
-76373
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1029079
100.0%

Most frequent character per block

ValueCountFrequency (%)
e161200
15.7%
C118183
11.5%
c118183
11.5%
h76373
7.4%
-76373
7.4%
k75166
7.3%
O75166
7.3%
u75166
7.3%
t75166
7.3%
a43017
 
4.2%
Other values (7)135086
13.1%

reservation_status_date
Categorical

HIGH CARDINALITY

Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2015-10-21
 
1461
2015-07-06
 
805
2016-11-25
 
790
2015-01-01
 
763
2016-01-18
 
625
Other values (921)
114946 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1193900
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)< 0.1%

Sample

1st row2015-07-01
2nd row2015-07-01
3rd row2015-07-02
4th row2015-07-02
5th row2015-07-03
ValueCountFrequency (%)
2015-10-211461
 
1.2%
2015-07-06805
 
0.7%
2016-11-25790
 
0.7%
2015-01-01763
 
0.6%
2016-01-18625
 
0.5%
2015-07-02469
 
0.4%
2016-12-07450
 
0.4%
2015-12-18423
 
0.4%
2016-02-09412
 
0.3%
2016-04-04382
 
0.3%
Other values (916)112810
94.5%
2021-05-09T00:54:46.892971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-10-211461
 
1.2%
2015-07-06805
 
0.7%
2016-11-25790
 
0.7%
2015-01-01763
 
0.6%
2016-01-18625
 
0.5%
2015-07-02469
 
0.4%
2016-12-07450
 
0.4%
2015-12-18423
 
0.4%
2016-02-09412
 
0.3%
2016-04-04382
 
0.3%
Other values (916)112810
94.5%

Most occurring characters

ValueCountFrequency (%)
0270055
22.6%
-238780
20.0%
1218578
18.3%
2187927
15.7%
679165
 
6.6%
760096
 
5.0%
546838
 
3.9%
326867
 
2.3%
823119
 
1.9%
921359
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number955120
80.0%
Dash Punctuation238780
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
0270055
28.3%
1218578
22.9%
2187927
19.7%
679165
 
8.3%
760096
 
6.3%
546838
 
4.9%
326867
 
2.8%
823119
 
2.4%
921359
 
2.2%
421116
 
2.2%
ValueCountFrequency (%)
-238780
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1193900
100.0%

Most frequent character per script

ValueCountFrequency (%)
0270055
22.6%
-238780
20.0%
1218578
18.3%
2187927
15.7%
679165
 
6.6%
760096
 
5.0%
546838
 
3.9%
326867
 
2.3%
823119
 
1.9%
921359
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1193900
100.0%

Most frequent character per block

ValueCountFrequency (%)
0270055
22.6%
-238780
20.0%
1218578
18.3%
2187927
15.7%
679165
 
6.6%
760096
 
5.0%
546838
 
3.9%
326867
 
2.3%
823119
 
1.9%
921359
 
1.8%

Interactions

2021-05-09T00:54:10.746351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:10.968657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:11.158661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:11.345072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:11.527469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:11.700763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:11.873158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:12.047576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:12.221184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:12.412122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:12.585143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:12.859744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:13.057529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:13.231838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:13.413698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:13.585384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:13.737098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:13.901005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:14.070630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:14.232400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:14.406682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:14.578378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:14.749749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:14.929464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:15.101676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:15.283927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:15.449395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:15.622387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:15.791969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:15.975900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:16.145501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:16.330546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:16.502884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:16.664993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:16.859436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:17.041284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:17.223190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:17.404829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:17.576275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:17.778959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:17.981509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:18.153644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:18.355750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:18.630579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:18.812899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:18.994431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:19.193218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:19.389655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:19.597741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:19.770633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:19.935092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:20.106468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:20.260468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:20.434522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:20.598135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:20.760218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:20.933858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:21.096604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:21.259320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:21.430731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:21.584277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:21.747347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:21.920578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:22.073328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:22.245108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:22.415270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:22.577735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:22.746968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:22.919972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:23.092090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:23.265169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:23.436069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:23.590642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:23.764292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:23.925915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:24.107732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:24.281049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:24.453495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:24.635718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:24.803019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:24.985172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:25.177183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:25.348991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:25.653890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:25.827703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:26.000157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:26.194752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:26.366007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:26.540124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:26.704471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:26.865612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:27.029157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:27.201638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:27.363351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:27.522985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:27.677790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:27.840623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:28.022484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:28.190445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:28.360958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:28.553752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:28.740293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:28.928784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:29.130778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:29.314396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:29.486582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:29.658564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:29.840374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:30.020183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:30.193838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:30.376082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:30.537375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:30.699071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:30.862028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.033635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.195271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.346627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.498626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.670189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.821754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:31.993822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:32.155599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:32.335179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:32.498760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:32.660766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:32.830371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:32.983819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:33.145257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:33.297035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:33.468697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:33.620239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-09T00:54:33.954987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-09T00:54:47.016090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-09T00:54:47.369679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-09T00:54:47.722773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-09T00:54:48.096707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-09T00:54:48.571382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-09T00:54:34.317302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-09T00:54:36.438299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Algarve Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No Depositno_agentno_company0Transient0.000Check-Out2015-07-01
1Algarve Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No Depositno_agentno_company0Transient0.000Check-Out2015-07-01
2Algarve Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No Depositno_agentno_company0Transient75.000Check-Out2015-07-02
3Algarve Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No DepositOtherno_company0Transient75.000Check-Out2015-07-02
4Algarve Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240no_company0Transient98.001Check-Out2015-07-03
5Algarve Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240no_company0Transient98.001Check-Out2015-07-03
6Algarve Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No Depositno_agentno_company0Transient107.000Check-Out2015-07-03
7Algarve Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No DepositOtherno_company0Transient103.001Check-Out2015-07-03
8Algarve Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240no_company0Transient82.001Canceled2015-05-06
9Algarve Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No DepositOtherno_company0Transient105.500Canceled2015-04-22

Last rows

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
119380Lisbon City Hotel0442017August35311320.00SCDEUOnline TATA/TO000AA0No Deposit9no_company0Transient140.7501Check-Out2017-09-04
119381Lisbon City Hotel01882017August35312320.00BBDEUDirectDirect000AA0No Deposit14no_company0Transient99.0000Check-Out2017-09-05
119382Lisbon City Hotel01352017August35302430.00BBJPNOnline TATA/TO000GG0No Deposit7no_company0Transient209.0000Check-Out2017-09-05
119383Lisbon City Hotel01642017August35312420.00BBDEUOffline TA/TOTA/TO000AA0No DepositOtherno_company0Transient87.6000Check-Out2017-09-06
119384Lisbon City Hotel0212017August35302520.00BBBELOffline TA/TOTA/TO000AA0No DepositOtherno_company0Transient96.1402Check-Out2017-09-06
119385Lisbon City Hotel0232017August35302520.00BBBELOffline TA/TOTA/TO000AA0No DepositOtherno_company0Transient96.1400Check-Out2017-09-06
119386Lisbon City Hotel01022017August35312530.00BBFRAOnline TATA/TO000EE0No Deposit9no_company0Transient225.4302Check-Out2017-09-07
119387Lisbon City Hotel0342017August35312520.00BBDEUOnline TATA/TO000DD0No Deposit9no_company0Transient157.7104Check-Out2017-09-07
119388Lisbon City Hotel01092017August35312520.00BBGBROnline TATA/TO000AA0No DepositOtherno_company0Transient104.4000Check-Out2017-09-07
119389Lisbon City Hotel02052017August35292720.00HBDEUOnline TATA/TO000AA0No Deposit9no_company0Transient151.2002Check-Out2017-09-07

Duplicate rows

Most frequent

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date# duplicates
7739Lisbon City Hotel12772016November4671220.00BBPRTGroupsTA/TO000AA0Non Refundno_agentno_company0Transient100.000Canceled2016-04-04180
6514Lisbon City Hotel1682016February8170220.00BBPRTGroupsTA/TO010AA0Non RefundOtherno_company0Transient75.000Canceled2016-01-06150
7409Lisbon City Hotel11882016June25150210.00BBPRTOffline TA/TOTA/TO000AA0Non RefundOtherno_company39Transient130.000Canceled2016-01-18109
7212Lisbon City Hotel11582016May22240210.00BBPRTGroupsTA/TO000AA0Non RefundOtherno_company31Transient130.000Canceled2016-01-18101
6183Lisbon City Hotel1342015December5080210.00BBPRTOffline TA/TOTA/TO010AA0Non RefundOtherno_company0Transient90.000Canceled2015-11-17100
6125Lisbon City Hotel1282017March920320.00BBPRTGroupsTA/TO000AA0Non Refundno_agentno_company0Transient95.000Canceled2017-02-0299
6239Lisbon City Hotel1382017January2140110.00BBPRTCorporateCorporate000AA0Non Refundno_agentOther0Transient75.000Canceled2016-12-0799
7205Lisbon City Hotel11562017April17260320.00BBPRTGroupsTA/TO000AA0Non RefundOtherno_company0Transient100.000Canceled2016-11-2199
6538Lisbon City Hotel1712016June25140310.00BBPRTOffline TA/TOTA/TO000AA0Non RefundOtherno_company0Transient120.000Canceled2016-04-2789
7272Lisbon City Hotel11662016November4510310.00BBPRTOffline TA/TOTA/TO000AA0Non RefundOtherno_company0Transient110.000Canceled2016-07-1385